Tight glycemic control with insulin therapy protocols in the intensive care unit (ICU) can reduce mortality and morbidity from stress-induced hyperglycemia, but this control comes with the risk of hypoglycemia. Computer simulation can be an essential tool in evaluating protocols for insulin delivery in this setting, and to this end, it is necessary to have mathematical models that explain blood glucose (BG) variability within this patient population.
Many models of normal glucose-insulin physiology have been developed. Examples include models based on the minimal model of Bergman, Cobelli, et al. [1], Hovorka et al.[2], and Dalla Man et al. [3] All have inputs for feeding 101, with parameters that describe an individual or average normal subject 102, and equations 103 that process this information to yield a series of normal BG values 104, as shown in FIG. 1.
There are no models of stress hyperglycemia per se. However, each existing model of normal glucose-insulin physiology can be modified to fit a real ICU patient on a “customized” or patient-by-patient basis in order to create a single, corresponding in silico ICU patient. Such per-patient ICU adaptation of normal glucose-insulin models has been done by Chase et al.[4], using a modified version of Bergman and Cobelli's minimal model, and by Hovorka et al., using a self-developed model [2]. Because their adaptations are not adequately based on the broader principles underlying the physiology of stress hyperglycemia, they must empirically adapt their full model for each real ICU patient to yield only a single “cloned” virtual ICU patient. In addition, this empirical adaptation of each model is specific to each model alone. This severely limits the number, variability, and ease of creation of in silico ICU patients for simulation.